Intrinsic Synchronization Analysis of Brain Activity in Obsessive-compulsive Disorders.

Obsessive-compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.

Attention Deficit Hyperactivity Disorder, Impulsivity, Anxiety, and Depression Symptoms Mediating the Relationship Between Childhood Trauma and Symptoms Severity of Obsessive-Compulsive Disorder.

INTRODUCTION: A growing body of research associates childhood trauma with obsessive-compulsive disorder (OCD). The aim of this study was to investigate the relationships between childhood trauma and OCD, including both its severity and OCD patients' comorbid impulsivity, ADHD, anxiety, and depressive symptoms. METHODS: A convenient sample consisting of 106 patients with OCD was given the Childhood Trauma Questionnaire (CTQ), Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Wender Utah Rating Scale (WURS), Hamilton Rating Scale for Depression (HAM-D), Beck Anxiety Inventory (BAI), and Barratt Impulsivity Scale-11 (BIS-11). RESULTS: The results showed that childhood trauma indirectly predicts the severity of OCD and directly predicts comorbidities in OCD patients, including anxiety, ADHD, WURS, and impulsivity. Patients with childhood trauma had higher WURS, BAI, and BIS-11 scores and fewer years of education. Ongoing adult ADHD was more common in individuals with childhood trauma. CONCLUSION: A history of childhood trauma in OCD patients has indirect effects on the severity of OCD and depressive symptoms and is associated with more severe anxiety, higher levels of impulsivity, higher prevalence of ADHD, and lower levels of education. More research is needed to clarify the effects of childhood trauma on OCD severity and comorbidity.

Prediction of treatment resistance in obsessive compulsive disorder patients based on EEG complexity as a biomarker.

OBJECTIVE: This study aimed to identify an Electroencephalography (EEG) complexity biomarker that could predict treatment resistance in Obsessive compulsive disorder (OCD) patients. Additionally, the statistical differences between EEG complexity values in treatment-resistant and treatment-responsive patients were determined. Moreover, the existence of correlations between EEG complexity and Yale-Brown Obsessive Compulsive Scale (YBOCS) score were evaluated. METHODS: EEG data for 29 treatment-resistant and 28 treatment-responsive OCD patients were retrospectively evaluated. Approximate entropy (ApEn) method was used to extract the EEG complexity from both whole EEG data and filtered EEG data, according to 4 common frequency bands, namely delta, theta, alpha, and beta. The random forests method was used to classify ApEn complexity. RESULTS: ApEn complexity extracted from beta band EEG segments discriminated treatment-responsive and treatment-resistant OCD patients with an accuracy of 89.66% (sensitivity: 89.44%; specificity: 90.64%). Beta band EEG complexity was lower in the treatment-resistant patients and the severity of OCD, as measured by YBOCS score, was inversely correlated with complexity values. CONCLUSIONS: The results indicate that, EEG complexity could be considered a biomarker for predicting treatment response in OCD patients. SIGNIFICANCE: The prediction of treatment response in OCD patients might help clinicians devise and administer individualized treatment plans.

Decreased global field synchronization of multichannel frontal EEG measurements in obsessive-compulsive disorders.

Global field synchronization (GFS) quantifies the synchronization level of brain oscillations. The GFS method has been introduced to measure functional synchronization of EEG data in the frequency domain. GFS also detects phase interactions between EEG signals acquired from all of the electrodes. If a considerable amount of local brain neurons has the same phase, these neurons appear to interact with each other. EEG data were received from 17 obsessive-compulsive disorder (OCD) patients and 17 healthy controls (HC). OCD effects on local and large-scale brain circuits were studied. Analysis of the GFS results showed significantly decreased values in the delta and full frequency bands. This research suggests that OCD causes synchronization disconnection in both the frontal and large-scale regions. This may be related to motivational, emotional and cognitive dysfunctions.

Seasonal mood changes in patients with obsessive-compulsive disorder.

Obsessive-compulsive disorder (OCD) is frequently associated with mood disorders. However, to date, the co-occurrence of OCD with seasonal affective disorder (SAD) has not been investigated. We have aimed to estimate the prevalence of seasonal mood changes in patients with OCD and explore the contribution of seasonality in mood to the severity of OCD. The Seasonal Pattern Assessment Questionnaire (SPAQ), the Yale-Brown Obsession and Compulsion Scale (Y-BOCS), the Hamilton Depression Rating Scale-17 Items (HDRS-17), and the Beck Anxiety Inventory (BAI) were administered to patients with OCD (n=104) and controls (n=125). The degree of seasonality was measured by the Global Seasonality Score (GSS) calculated from the SPAQ. SAD and subsyndromal seasonal affective disorder (S-SAD) were significantly more prevalent in patients with OCD (53%, n=55) than controls (25%, n=31). When patients were assessed in the season in which SAD occurs, depression and compulsions (but not obsessions, OCD or anxiety) were more severe than those assessed in a season during which SAD does not occur. SAD frequently co-occurs with OCD and, given this co-occurrence, depression symptoms in some patients with OCD might be expected to vary on a seasonal basis.

Obsessive-compulsive adults with and without childhood ADHD symptoms.

Obsessive-compulsive disorder (OCD) and attention-deficit and hyperactivity disorder (ADHD) frequently coexist. To understand whether childhood ADHD can increase the risk of OCD in adulthood and whether it influences the phenomenology of OCD, we investigated the symptoms of ADHD during childhood in obsessive-compulsive adults who had never been diagnosed as ADHD. Adults with OCD (n = 83) were given the Wender Utah Rating Scale (WURS), Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Barratt Impulsiveness Scale-11 (BIS-11), Hamilton Depression Rating Scale-17 (HDRS-17) and Beck Anxiety Inventory (BAI). The prevalence of childhood ADHD symptoms was 40.9 % (n = 34) and that of adult ADHD was 16.9 % (n = 14). Patients with childhood ADHD symptoms had an earlier onset of OCD, higher scores of the BAI and BIS-11. The scores of the Y-BOCS and HDRS-17 did not differ between those having and not having childhood ADHD symptoms. Childhood history of ADHD symptoms is common in adult OCD patients who have never been diagnosed as ADHD. Childhood ADHD symptoms are associated with an earlier age of OCD, more severe anxiety and higher impulsiveness. Even remitted ADHD may be a risk factor for OCD in later life.

Symptom Dimensions, Smoking and Impulsiveness in Obsessive-Compulsive Disorder.

BACKGROUND: Obsessive-compulsive disorder (OCD) has distinct symptom dimensions with possibly subtle differences in the underlying neurobiology. One behavioral habit, smoking, has been widely investigated in psychiatric disorders, though received less attention in OCD. Here, we aimed to investigate the relationship between symptom dimensions and smoking behavior in OCD. SUBJECTS AND METHODS: OCD patients (n=167) with the symptom dimensions of washing, taboo thoughts and symmetry-counting-repeating-ordering (S+C+R+O) were questioned in terms of smoking status and assessed with the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Fagerström Test for Nicotine Dependence (FTND), Hamilton Depression Rating Scale-17 Items (HDRS-17), Beck Anxiety Inventory (BAI) and Barratt Impulsiveness Scale-11 (BIS 11). RESULTS: Smoking status differed significantly among patients with distinct symptom dimensions (p=0.009).The ratio of smokers was the lowest in those with the washing (30%, N=12) and the highest in the S+C+R+O (68.2%, N=15) group. Those with taboo thoughts had a smoking ratio of 37.14% (N=39). In post hoc analysis, smoking ratio was significantly higher in the S+C+R+O group than in those with washing symptoms (p=0.004) and taboo thoughts (p=0.007) though it did not differ significantly between washers and taboo thought groups. The BIS-11 did not differ across symptom dimensions. CONCLUSIONS: OCD is a heterogeneous disorder in terms of smoking. Impulsiveness, which does not significantly vary across distinct symptom dimensions, cannot explain this heterogeneity. The severity of addiction does not differ in smokers with OCD across symptom dimensions.

Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements.

In the present study, both single channel electroencephalography (EEG) complexity and two channel interhemispheric dependency measurements have newly been examined for classification of patients with obsessive-compulsive disorder (OCD) and controls by using support vector machine classifiers. Three embedding entropy measurements (approximate entropy, sample entropy, permutation entropy (PermEn)) are used to estimate single channel EEG complexity for 19-channel eyes closed cortical measurements. Mean coherence and mutual information are examined to measure the level of interhemispheric dependency in frequency and statistical domain, respectively for eight distinct electrode pairs placed on the scalp with respect to the international 10-20 electrode placement system. All methods are applied to short EEG segments of 2 s. The classification performance is measured 20 times with different 2-fold cross-validation data for both single channel complexity features (19 features) and interhemispheric dependency features (eight features). The highest classification accuracy of 85 ±5.2% is provided by PermEn at prefrontal regions of the brain. Even if the classification success do not provided by other methods as high as PermEn, the clear differences between patients and controls at prefrontal regions can also be obtained by using other methods except coherence. In conclusion, OCD, defined as illness of orbitofronto-striatal structures [Beucke et al., JAMA Psychiatry70 (2013) 619-629; Cavedini et al., Psychiatry Res.78 (1998) 21-28; Menzies et al., Neurosci. Biobehav. Rev.32(3) (2008) 525-549], is caused by functional abnormalities in the pre-frontal regions. Particularly, patients are characterized by lower EEG complexity at both pre-frontal regions and right fronto-temporal locations. Our results are compatible with imaging studies that define OCD as a sub group of anxiety disorders exhibited a decreased complexity (such as anorexia nervosa [Toth et al., Int. J. Psychophysiol.51(3) (2004) 253-260] and panic disorder [Bob et al., Physiol. Res.55 (2006) S113-S119]).

EEG power, cordance and coherence differences between unipolar and bipolar depression.

INTRODUCTION: Understanding the biological underpinnings of unipolar (UD) and bipolar depression (BD) is vital for avoiding inappropriate treatment through the misdiagnosis of bipolar patients in their first depressive episode. One plausible way to distinguish between UD and BD is to compare EEG brain dynamics to identify potential neurophysiological biomarkers. Here we aimed to test group differences in EEG power, cordance and coherence values between UD and BD. METHODS: Twenty-five bipolar and 56 unipolar depression patients were recruited. Sociodemographic and clinical variables were collected in addition to resting state EEG. Data was analyzed with multivariate and repeated analyses of variance where parametric assumptions were met. RESULTS: Accordingly, we did not find any differences in the EEG absolute power and frontal asymmetry indexes between UD and BD. Regarding cordance, significant group differences were observed in the right theta cordance values (p=0.031). Regarding coherence, BD patients (as compared to UD) exhibited greater central-temporal theta (p=0.003), and parietal-temporal alpha (p=0.007) and theta (p=0.001) coherence. Lastly, less alpha coherence in BD was present at right frontal-central (p=0.007) and central inter-hemispheric (p=0.019) regions. CONCLUSIONS: Our results demonstrate that EEG cordance and coherence values have potential to discriminate between UD and BD. The loss of temporal synchronization in the frontal interhemispheric and right sided frontolimbic neuronal networks may be a unique feature that distinguishes between BD and UD.

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

Transcranial magnetic stimulation for treating depression in elderly patients.

PURPOSE: The aim of the study reported here was to examine the safety and effectiveness of high-frequency repetitive transcranial magnetic stimulation (rTMS) in elderly patients with depression. PATIENTS AND METHODS: Sixty-five depressed elderly patients received rTMS over their left prefrontal cortex for 6 days per week, from Monday to Saturday, for 3 weeks. The rTMS intensity was set at 100% of the motor threshold and 25 Hz stimulation with a duration of 2 seconds and was delivered 20 times at 30-second intervals. A full course comprised an average of 1000 magnetic pulses. Depression was rated using the Hamilton Depression Rating Scale (HAMD) before and after treatment. Response was defined as a 50% reduction in HAMD score. Patients with HAMD scores < 8 were considered to be in remission. RESULTS: The mean HAMD score for the study group decreased from 21.94 ± 5.12 before treatment to 11.28 ± 4.56 after rTMS (P < 0.001). Following the treatment period, 58.46% of the study group demonstrated significant mood improvement, as indexed by a reduction of more than 50% on the HAMD score. Nineteen of these 38 patients attained remission (HAMD score < 8), while 41.54% of all study patients achieved a partial response. None of the patients had a worsened HAMD score at the end of the treatment. Treatment was generally well tolerated and no serious adverse effects were reported. CONCLUSION: In this study, rTMS was found to be a safe, well-tolerated treatment, and a useful adjunctive treatment to medications in elderly treatment-resistant depressed patients. This study contributes to the existing evidence on the antidepressant effect of rTMS in the treatment of depression in patients over 60 years of age.

Efficacy of high-frequency repetitive transcranial magnetic stimulation in treatment-resistant depression.

We examined the efficacy of high-frequency repetitive transcranial magnetic stimulation (rTMS) in 419 patients with treatment-resistant depression. The patients received daily sessions of rTMS over the left prefrontal cortex as an adjuvant to pharmacotherapy. The rTMS intensity was set at 100% of the motor threshold and 25 Hz stimulation, with train duration of 2 seconds delivered at 30-second intervals. A full course comprised 1000 magnetic pulses. Depression was rated using the Hamilton Depression Rating Scale (HAMD) before and after treatment. Response was defined as a 50% reduction in the HAMD score. Patients with HAMD scores of less than 8 were considered to be in remission. The mean HAMD score for the study group decreased from 22.59 ± 5.92 to 10.50 ± 5.83 (P < .001). After the treatment period, 268 (64%) out of 419 patients demonstrated significant mood improvements, as indexed by a reduction of more than 50% on the HAMD score. In addition, 140 patients (33.4%) attained remission (HAMD score <8); and 11 patients achieved a partial response. Treatment was generally well tolerated, and no serious adverse effects were reported. In conclusion, high-frequency (25 Hz) rTMS was well tolerated and found to be statistically and clinically effective in patients with treatment-resistant depression. This study contributed to the existing evidence of the antidepressant effect of rTMS in the treatment of depression.